1256 lines
48 KiB
Python
1256 lines
48 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""The arguments of the server."""
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import argparse
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import dataclasses
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import logging
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import os
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import random
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import tempfile
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from typing import List, Optional
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from sglang.srt.hf_transformers_utils import check_gguf_file
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from sglang.srt.reasoning_parser import ReasoningParser
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from sglang.srt.utils import (
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configure_ipv6,
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get_amdgpu_memory_capacity,
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get_device,
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get_hpu_memory_capacity,
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get_nvgpu_memory_capacity,
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is_cuda,
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is_flashinfer_available,
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is_hip,
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is_port_available,
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is_remote_url,
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is_valid_ipv6_address,
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nullable_str,
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)
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logger = logging.getLogger(__name__)
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@dataclasses.dataclass
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class ServerArgs:
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# Model and tokenizer
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model_path: str
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tokenizer_path: Optional[str] = None
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tokenizer_mode: str = "auto"
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skip_tokenizer_init: bool = False
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load_format: str = "auto"
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trust_remote_code: bool = False
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dtype: str = "auto"
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kv_cache_dtype: str = "auto"
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quantization: Optional[str] = None
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quantization_param_path: Optional[str] = None
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context_length: Optional[int] = None
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device: Optional[str] = None
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served_model_name: Optional[str] = None
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chat_template: Optional[str] = None
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completion_template: Optional[str] = None
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is_embedding: bool = False
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revision: Optional[str] = None
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# Port for the HTTP server
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host: str = "127.0.0.1"
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port: int = 30000
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# Memory and scheduling
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mem_fraction_static: Optional[float] = None
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max_running_requests: Optional[int] = None
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max_total_tokens: Optional[int] = None
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chunked_prefill_size: Optional[int] = None
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max_prefill_tokens: int = 16384
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schedule_policy: str = "fcfs"
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schedule_conservativeness: float = 1.0
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cpu_offload_gb: int = 0
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page_size: int = 1
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# Other runtime options
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tp_size: int = 1
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stream_interval: int = 1
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stream_output: bool = False
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random_seed: Optional[int] = None
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constrained_json_whitespace_pattern: Optional[str] = None
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watchdog_timeout: float = 300
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dist_timeout: Optional[int] = None # timeout for torch.distributed
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download_dir: Optional[str] = None
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base_gpu_id: int = 0
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gpu_id_step: int = 1
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# Logging
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log_level: str = "info"
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log_level_http: Optional[str] = None
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log_requests: bool = False
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log_requests_level: int = 0
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show_time_cost: bool = False
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enable_metrics: bool = False
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decode_log_interval: int = 40
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# API related
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api_key: Optional[str] = None
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file_storage_path: str = "sglang_storage"
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enable_cache_report: bool = False
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reasoning_parser: Optional[str] = None
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# Data parallelism
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dp_size: int = 1
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load_balance_method: str = "round_robin"
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# Expert parallelism
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ep_size: int = 1
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# Multi-node distributed serving
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dist_init_addr: Optional[str] = None
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nnodes: int = 1
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node_rank: int = 0
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# Model override args in JSON
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json_model_override_args: str = "{}"
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# LoRA
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lora_paths: Optional[List[str]] = None
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max_loras_per_batch: int = 8
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lora_backend: str = "triton"
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# Kernel backend
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attention_backend: Optional[str] = None
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sampling_backend: Optional[str] = None
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grammar_backend: Optional[str] = "xgrammar"
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# Speculative decoding
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speculative_algorithm: Optional[str] = None
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speculative_draft_model_path: Optional[str] = None
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speculative_num_steps: int = 5
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speculative_eagle_topk: int = 4
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speculative_num_draft_tokens: int = 8
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speculative_accept_threshold_single: float = 1.0
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speculative_accept_threshold_acc: float = 1.0
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speculative_token_map: Optional[str] = None
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# Double Sparsity
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enable_double_sparsity: bool = False
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ds_channel_config_path: Optional[str] = None
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ds_heavy_channel_num: int = 32
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ds_heavy_token_num: int = 256
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ds_heavy_channel_type: str = "qk"
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ds_sparse_decode_threshold: int = 4096
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# Optimization/debug options
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disable_radix_cache: bool = False
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disable_cuda_graph: bool = False
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disable_cuda_graph_padding: bool = False
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enable_nccl_nvls: bool = False
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disable_outlines_disk_cache: bool = False
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disable_custom_all_reduce: bool = False
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disable_mla: bool = False
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disable_overlap_schedule: bool = False
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enable_mixed_chunk: bool = False
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enable_dp_attention: bool = False
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enable_ep_moe: bool = False
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enable_deepep_moe: bool = False
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enable_torch_compile: bool = False
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torch_compile_max_bs: int = 32
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cuda_graph_max_bs: Optional[int] = None
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cuda_graph_bs: Optional[List[int]] = None
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torchao_config: str = ""
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enable_nan_detection: bool = False
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enable_p2p_check: bool = False
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triton_attention_reduce_in_fp32: bool = False
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triton_attention_num_kv_splits: int = 8
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num_continuous_decode_steps: int = 1
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delete_ckpt_after_loading: bool = False
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enable_memory_saver: bool = False
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allow_auto_truncate: bool = False
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enable_custom_logit_processor: bool = False
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tool_call_parser: Optional[str] = None
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enable_hierarchical_cache: bool = False
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hicache_ratio: float = 2.0
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enable_flashinfer_mla: bool = False
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enable_flashmla: bool = False
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flashinfer_mla_disable_ragged: bool = False
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warmups: Optional[str] = None
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# Debug tensor dumps
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debug_tensor_dump_output_folder: Optional[str] = None
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debug_tensor_dump_input_file: Optional[str] = None
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debug_tensor_dump_inject: bool = False
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# For PD disaggregation: can be "null" (not disaggregated), "prefill" (prefill-only), or "decode" (decode-only)
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disaggregation_mode: str = "null"
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disaggregation_bootstrap_port: int = 8998
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def __post_init__(self):
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# Set missing default values
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if self.tokenizer_path is None:
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self.tokenizer_path = self.model_path
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if self.device is None:
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self.device = get_device()
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if self.served_model_name is None:
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self.served_model_name = self.model_path
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if self.random_seed is None:
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self.random_seed = random.randint(0, 1 << 30)
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if is_cuda():
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gpu_mem = get_nvgpu_memory_capacity()
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elif is_hip():
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gpu_mem = get_amdgpu_memory_capacity()
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elif self.device == "hpu":
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gpu_mem = get_hpu_memory_capacity()
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else:
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# GPU memory is not known yet or no GPU is available.
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gpu_mem = None
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# Set mem fraction static, which depends on the tensor parallelism size
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if self.mem_fraction_static is None:
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if self.tp_size >= 16:
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self.mem_fraction_static = 0.79
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elif self.tp_size >= 8:
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self.mem_fraction_static = 0.81
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elif self.tp_size >= 4:
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self.mem_fraction_static = 0.85
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elif self.tp_size >= 2:
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self.mem_fraction_static = 0.87
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else:
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self.mem_fraction_static = 0.88
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# Set chunked prefill size, which depends on the gpu memory capacity
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if self.chunked_prefill_size is None:
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if gpu_mem is not None and gpu_mem < 25_000:
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self.chunked_prefill_size = 2048
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else:
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self.chunked_prefill_size = 8192
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assert self.chunked_prefill_size % self.page_size == 0
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if self.enable_flashmla is True:
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logger.warning(
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"FlashMLA only supports a page_size of 64, change page_size to 64."
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)
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self.page_size = 64
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# Set cuda graph max batch size
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if self.cuda_graph_max_bs is None:
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# Based on detailed statistics, when serving TP1/TP2 models on lower-end GPUs with HBM<25G, you can either disable cuda graph or set `cuda_graph_max_bs` to a very small value to reduce the memory overhead of creating cuda graphs, with almost no impact on performance. However, when serving models with TP4 or TP8, we need to enable cuda graph to maintain high performance. In this case, we can set `cuda_graph_max_bs` to 80 (half of the default value 160) to reduce the memory overhead of creating cuda graphs. Looking at the logs from TP4 serving of qwen2-72b, a value of 80 is sufficient and can reduce the memory overhead of creating cuda graphs on lower-end GPUs compared to the original 160, avoiding OOM issues.
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if gpu_mem is not None and gpu_mem < 25_000:
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if self.tp_size < 4:
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self.cuda_graph_max_bs = 8
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else:
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self.cuda_graph_max_bs = 80
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else:
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self.cuda_graph_max_bs = 160
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# Choose kernel backends
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if self.device == "hpu":
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self.attention_backend = "torch_native"
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self.sampling_backend = "pytorch"
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if self.attention_backend is None:
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self.attention_backend = (
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"flashinfer" if is_flashinfer_available() else "triton"
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)
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if self.sampling_backend is None:
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self.sampling_backend = (
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"flashinfer" if is_flashinfer_available() else "pytorch"
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)
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if self.attention_backend == "torch_native":
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logger.warning(
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"Cuda graph is disabled because of using torch native attention backend"
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)
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self.disable_cuda_graph = True
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# Expert parallelism
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if self.enable_ep_moe:
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self.ep_size = self.tp_size
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logger.info(
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f"EP MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
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)
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# Data parallelism attention
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if self.enable_dp_attention:
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self.schedule_conservativeness = self.schedule_conservativeness * 0.3
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assert (
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self.dp_size > 1
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), "Please set a dp-size > 1. You can use 1 < dp-size <= tp-size "
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assert self.tp_size % self.dp_size == 0
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self.chunked_prefill_size = self.chunked_prefill_size // self.dp_size
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logger.warning(
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f"DP attention is enabled. The chunked prefill size is adjusted to {self.chunked_prefill_size} to avoid MoE kernel issues. "
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)
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self.enable_sp_layernorm = False
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# DeepEP MoE
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if self.enable_deepep_moe:
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self.ep_size = self.tp_size
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self.enable_sp_layernorm = (
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self.dp_size < self.tp_size if self.enable_dp_attention else True
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)
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logger.info(
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f"DeepEP MoE is enabled. The expert parallel size is adjusted to be the same as the tensor parallel size[{self.tp_size}]."
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)
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# Speculative Decoding
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if self.speculative_algorithm == "NEXTN":
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# NEXTN shares the same implementation of EAGLE
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self.speculative_algorithm = "EAGLE"
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if (
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self.speculative_algorithm == "EAGLE"
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or self.speculative_algorithm == "EAGLE3"
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):
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if self.max_running_requests is None:
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self.max_running_requests = 32
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self.disable_overlap_schedule = True
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logger.info(
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"Overlap scheduler is disabled because of using "
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"eagle speculative decoding."
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)
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# The token generated from the verify step is counted.
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# If sepculative_num_steps >= speculative_num_draft_tokens, the additional tokens will definitely be discarded.
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# assert self.speculative_num_steps < self.speculative_num_draft_tokens
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# GGUF
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if (
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self.load_format == "auto" or self.load_format == "gguf"
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) and check_gguf_file(self.model_path):
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self.quantization = self.load_format = "gguf"
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if is_remote_url(self.model_path):
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self.load_format = "remote"
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# AMD-specific Triton attention KV splits default number
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if is_hip():
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self.triton_attention_num_kv_splits = 16
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# PD disaggregation
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if self.disaggregation_mode == "prefill":
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self.disable_cuda_graph = True
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logger.warning("KV cache is forced as chunk cache for decode server")
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self.disable_overlap_schedule = True
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logger.warning("Overlap scheduler is disabled for prefill server")
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elif self.disaggregation_mode == "decode":
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self.disable_radix_cache = True
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logger.warning("Cuda graph is disabled for prefill server")
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self.disable_overlap_schedule = True
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logger.warning("Overlap scheduler is disabled for decode server")
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os.environ["SGLANG_ENABLE_TORCH_COMPILE"] = (
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"1" if self.enable_torch_compile else "0"
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)
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@staticmethod
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def add_cli_args(parser: argparse.ArgumentParser):
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# Model and port args
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parser.add_argument(
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"--model-path",
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type=str,
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help="The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
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required=True,
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)
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parser.add_argument(
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"--tokenizer-path",
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type=str,
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default=ServerArgs.tokenizer_path,
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help="The path of the tokenizer.",
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)
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parser.add_argument(
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"--host", type=str, default=ServerArgs.host, help="The host of the server."
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)
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parser.add_argument(
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"--port", type=int, default=ServerArgs.port, help="The port of the server."
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)
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parser.add_argument(
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"--tokenizer-mode",
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type=str,
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default=ServerArgs.tokenizer_mode,
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choices=["auto", "slow"],
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help="Tokenizer mode. 'auto' will use the fast "
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"tokenizer if available, and 'slow' will "
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"always use the slow tokenizer.",
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)
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parser.add_argument(
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"--skip-tokenizer-init",
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action="store_true",
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help="If set, skip init tokenizer and pass input_ids in generate request",
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)
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parser.add_argument(
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"--load-format",
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type=str,
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default=ServerArgs.load_format,
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choices=[
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"auto",
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"pt",
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"safetensors",
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"npcache",
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"dummy",
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"sharded_state",
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"gguf",
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"bitsandbytes",
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"layered",
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"remote",
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],
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help="The format of the model weights to load. "
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'"auto" will try to load the weights in the safetensors format '
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"and fall back to the pytorch bin format if safetensors format "
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"is not available. "
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'"pt" will load the weights in the pytorch bin format. '
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'"safetensors" will load the weights in the safetensors format. '
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'"npcache" will load the weights in pytorch format and store '
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"a numpy cache to speed up the loading. "
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'"dummy" will initialize the weights with random values, '
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"which is mainly for profiling."
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'"gguf" will load the weights in the gguf format. '
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'"bitsandbytes" will load the weights using bitsandbytes '
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"quantization."
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'"layered" loads weights layer by layer so that one can quantize a '
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"layer before loading another to make the peak memory envelope "
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"smaller.",
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)
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parser.add_argument(
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"--trust-remote-code",
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action="store_true",
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help="Whether or not to allow for custom models defined on the Hub in their own modeling files.",
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)
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parser.add_argument(
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"--dtype",
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type=str,
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default=ServerArgs.dtype,
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choices=["auto", "half", "float16", "bfloat16", "float", "float32"],
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help="Data type for model weights and activations.\n\n"
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'* "auto" will use FP16 precision for FP32 and FP16 models, and '
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"BF16 precision for BF16 models.\n"
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'* "half" for FP16. Recommended for AWQ quantization.\n'
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'* "float16" is the same as "half".\n'
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'* "bfloat16" for a balance between precision and range.\n'
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'* "float" is shorthand for FP32 precision.\n'
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'* "float32" for FP32 precision.',
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)
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parser.add_argument(
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"--kv-cache-dtype",
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type=str,
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default=ServerArgs.kv_cache_dtype,
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choices=["auto", "fp8_e5m2", "fp8_e4m3"],
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help='Data type for kv cache storage. "auto" will use model data type. "fp8_e5m2" and "fp8_e4m3" is supported for CUDA 11.8+.',
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)
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parser.add_argument(
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"--quantization",
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type=str,
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default=ServerArgs.quantization,
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choices=[
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"awq",
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"fp8",
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"gptq",
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"marlin",
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"gptq_marlin",
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"awq_marlin",
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"bitsandbytes",
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"gguf",
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"modelopt",
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"w8a8_int8",
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"w8a8_fp8",
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],
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help="The quantization method.",
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)
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parser.add_argument(
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"--quantization-param-path",
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type=nullable_str,
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default=None,
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help="Path to the JSON file containing the KV cache "
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"scaling factors. This should generally be supplied, when "
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"KV cache dtype is FP8. Otherwise, KV cache scaling factors "
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"default to 1.0, which may cause accuracy issues. ",
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)
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parser.add_argument(
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"--context-length",
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type=int,
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default=ServerArgs.context_length,
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help="The model's maximum context length. Defaults to None (will use the value from the model's config.json instead).",
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)
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parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default=ServerArgs.device,
|
|
help="The device to use ('cuda', 'xpu', 'hpu', 'cpu'). Defaults to auto-detection if not specified.",
|
|
)
|
|
parser.add_argument(
|
|
"--served-model-name",
|
|
type=str,
|
|
default=ServerArgs.served_model_name,
|
|
help="Override the model name returned by the v1/models endpoint in OpenAI API server.",
|
|
)
|
|
parser.add_argument(
|
|
"--chat-template",
|
|
type=str,
|
|
default=ServerArgs.chat_template,
|
|
help="The buliltin chat template name or the path of the chat template file. This is only used for OpenAI-compatible API server.",
|
|
)
|
|
parser.add_argument(
|
|
"--completion-template",
|
|
type=str,
|
|
default=ServerArgs.completion_template,
|
|
help="The buliltin completion template name or the path of the completion template file. This is only used for OpenAI-compatible API server. only for code completion currently.",
|
|
)
|
|
parser.add_argument(
|
|
"--is-embedding",
|
|
action="store_true",
|
|
help="Whether to use a CausalLM as an embedding model.",
|
|
)
|
|
parser.add_argument(
|
|
"--revision",
|
|
type=str,
|
|
default=None,
|
|
help="The specific model version to use. It can be a branch "
|
|
"name, a tag name, or a commit id. If unspecified, will use "
|
|
"the default version.",
|
|
)
|
|
# Memory and scheduling
|
|
parser.add_argument(
|
|
"--mem-fraction-static",
|
|
type=float,
|
|
default=ServerArgs.mem_fraction_static,
|
|
help="The fraction of the memory used for static allocation (model weights and KV cache memory pool). Use a smaller value if you see out-of-memory errors.",
|
|
)
|
|
parser.add_argument(
|
|
"--max-running-requests",
|
|
type=int,
|
|
default=ServerArgs.max_running_requests,
|
|
help="The maximum number of running requests.",
|
|
)
|
|
parser.add_argument(
|
|
"--max-total-tokens",
|
|
type=int,
|
|
default=ServerArgs.max_total_tokens,
|
|
help="The maximum number of tokens in the memory pool. If not specified, it will be automatically calculated based on the memory usage fraction. "
|
|
"This option is typically used for development and debugging purposes.",
|
|
)
|
|
parser.add_argument(
|
|
"--chunked-prefill-size",
|
|
type=int,
|
|
default=ServerArgs.chunked_prefill_size,
|
|
help="The maximum number of tokens in a chunk for the chunked prefill. Setting this to -1 means disabling chunked prefill.",
|
|
)
|
|
parser.add_argument(
|
|
"--max-prefill-tokens",
|
|
type=int,
|
|
default=ServerArgs.max_prefill_tokens,
|
|
help="The maximum number of tokens in a prefill batch. The real bound will be the maximum of this value and the model's maximum context length.",
|
|
)
|
|
parser.add_argument(
|
|
"--schedule-policy",
|
|
type=str,
|
|
default=ServerArgs.schedule_policy,
|
|
choices=["lpm", "random", "fcfs", "dfs-weight"],
|
|
help="The scheduling policy of the requests.",
|
|
)
|
|
parser.add_argument(
|
|
"--schedule-conservativeness",
|
|
type=float,
|
|
default=ServerArgs.schedule_conservativeness,
|
|
help="How conservative the schedule policy is. A larger value means more conservative scheduling. Use a larger value if you see requests being retracted frequently.",
|
|
)
|
|
parser.add_argument(
|
|
"--cpu-offload-gb",
|
|
type=int,
|
|
default=ServerArgs.cpu_offload_gb,
|
|
help="How many GBs of RAM to reserve for CPU offloading.",
|
|
)
|
|
parser.add_argument(
|
|
"--page-size",
|
|
type=int,
|
|
default=ServerArgs.page_size,
|
|
help="The number of tokens in a page.",
|
|
)
|
|
|
|
# Other runtime options
|
|
parser.add_argument(
|
|
"--tensor-parallel-size",
|
|
"--tp-size",
|
|
type=int,
|
|
default=ServerArgs.tp_size,
|
|
help="The tensor parallelism size.",
|
|
)
|
|
parser.add_argument(
|
|
"--stream-interval",
|
|
type=int,
|
|
default=ServerArgs.stream_interval,
|
|
help="The interval (or buffer size) for streaming in terms of the token length. A smaller value makes streaming smoother, while a larger value makes the throughput higher",
|
|
)
|
|
parser.add_argument(
|
|
"--stream-output",
|
|
action="store_true",
|
|
help="Whether to output as a sequence of disjoint segments.",
|
|
)
|
|
parser.add_argument(
|
|
"--random-seed",
|
|
type=int,
|
|
default=ServerArgs.random_seed,
|
|
help="The random seed.",
|
|
)
|
|
parser.add_argument(
|
|
"--constrained-json-whitespace-pattern",
|
|
type=str,
|
|
default=ServerArgs.constrained_json_whitespace_pattern,
|
|
help=r"Regex pattern for syntactic whitespaces allowed in JSON constrained output. For example, to allow the model generate consecutive whitespaces, set the pattern to [\n\t ]*",
|
|
)
|
|
parser.add_argument(
|
|
"--watchdog-timeout",
|
|
type=float,
|
|
default=ServerArgs.watchdog_timeout,
|
|
help="Set watchdog timeout in seconds. If a forward batch takes longer than this, the server will crash to prevent hanging.",
|
|
)
|
|
parser.add_argument(
|
|
"--dist-timeout",
|
|
type=int,
|
|
default=ServerArgs.dist_timeout,
|
|
help="Set timeout for torch.distributed initialization.",
|
|
)
|
|
parser.add_argument(
|
|
"--download-dir",
|
|
type=str,
|
|
default=ServerArgs.download_dir,
|
|
help="Model download directory for huggingface.",
|
|
)
|
|
parser.add_argument(
|
|
"--base-gpu-id",
|
|
type=int,
|
|
default=ServerArgs.base_gpu_id,
|
|
help="The base GPU ID to start allocating GPUs from. Useful when running multiple instances on the same machine.",
|
|
)
|
|
parser.add_argument(
|
|
"--gpu-id-step",
|
|
type=int,
|
|
default=ServerArgs.gpu_id_step,
|
|
help="The delta between consecutive GPU IDs that are used. For example, setting it to 2 will use GPU 0,2,4,...",
|
|
)
|
|
|
|
# Logging
|
|
parser.add_argument(
|
|
"--log-level",
|
|
type=str,
|
|
default=ServerArgs.log_level,
|
|
help="The logging level of all loggers.",
|
|
)
|
|
parser.add_argument(
|
|
"--log-level-http",
|
|
type=str,
|
|
default=ServerArgs.log_level_http,
|
|
help="The logging level of HTTP server. If not set, reuse --log-level by default.",
|
|
)
|
|
parser.add_argument(
|
|
"--log-requests",
|
|
action="store_true",
|
|
help="Log metadata, inputs, outputs of all requests. The verbosity is decided by --log-requests-level",
|
|
)
|
|
parser.add_argument(
|
|
"--log-requests-level",
|
|
type=int,
|
|
default=0,
|
|
help="0: Log metadata. 1. Log metadata and partial input/output. 2. Log every input/output.",
|
|
choices=[0, 1, 2],
|
|
)
|
|
parser.add_argument(
|
|
"--show-time-cost",
|
|
action="store_true",
|
|
help="Show time cost of custom marks.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-metrics",
|
|
action="store_true",
|
|
help="Enable log prometheus metrics.",
|
|
)
|
|
parser.add_argument(
|
|
"--decode-log-interval",
|
|
type=int,
|
|
default=ServerArgs.decode_log_interval,
|
|
help="The log interval of decode batch.",
|
|
)
|
|
|
|
# API related
|
|
parser.add_argument(
|
|
"--api-key",
|
|
type=str,
|
|
default=ServerArgs.api_key,
|
|
help="Set API key of the server. It is also used in the OpenAI API compatible server.",
|
|
)
|
|
parser.add_argument(
|
|
"--file-storage-path",
|
|
type=str,
|
|
default=ServerArgs.file_storage_path,
|
|
help="The path of the file storage in backend.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-cache-report",
|
|
action="store_true",
|
|
help="Return number of cached tokens in usage.prompt_tokens_details for each openai request.",
|
|
)
|
|
parser.add_argument(
|
|
"--reasoning-parser",
|
|
type=str,
|
|
choices=list(ReasoningParser.DetectorMap.keys()),
|
|
default=ServerArgs.reasoning_parser,
|
|
help=f"Specify the parser for reasoning models, supported parsers are: {list(ReasoningParser.DetectorMap.keys())}.",
|
|
)
|
|
|
|
# Data parallelism
|
|
parser.add_argument(
|
|
"--data-parallel-size",
|
|
"--dp-size",
|
|
type=int,
|
|
default=ServerArgs.dp_size,
|
|
help="The data parallelism size.",
|
|
)
|
|
parser.add_argument(
|
|
"--load-balance-method",
|
|
type=str,
|
|
default=ServerArgs.load_balance_method,
|
|
help="The load balancing strategy for data parallelism.",
|
|
choices=[
|
|
"round_robin",
|
|
"shortest_queue",
|
|
],
|
|
)
|
|
|
|
# Expert parallelism
|
|
parser.add_argument(
|
|
"--expert-parallel-size",
|
|
"--ep-size",
|
|
type=int,
|
|
default=ServerArgs.ep_size,
|
|
help="The expert parallelism size.",
|
|
)
|
|
|
|
# Multi-node distributed serving
|
|
parser.add_argument(
|
|
"--dist-init-addr",
|
|
"--nccl-init-addr", # For backward compatbility. This will be removed in the future.
|
|
type=str,
|
|
help="The host address for initializing distributed backend (e.g., `192.168.0.2:25000`).",
|
|
)
|
|
parser.add_argument(
|
|
"--nnodes", type=int, default=ServerArgs.nnodes, help="The number of nodes."
|
|
)
|
|
parser.add_argument(
|
|
"--node-rank", type=int, default=ServerArgs.node_rank, help="The node rank."
|
|
)
|
|
|
|
# Model override args
|
|
parser.add_argument(
|
|
"--json-model-override-args",
|
|
type=str,
|
|
help="A dictionary in JSON string format used to override default model configurations.",
|
|
default=ServerArgs.json_model_override_args,
|
|
)
|
|
|
|
# LoRA
|
|
parser.add_argument(
|
|
"--lora-paths",
|
|
type=str,
|
|
nargs="*",
|
|
default=None,
|
|
action=LoRAPathAction,
|
|
help="The list of LoRA adapters. You can provide a list of either path in str or renamed path in the format {name}={path}.",
|
|
)
|
|
parser.add_argument(
|
|
"--max-loras-per-batch",
|
|
type=int,
|
|
default=8,
|
|
help="Maximum number of adapters for a running batch, include base-only request.",
|
|
)
|
|
parser.add_argument(
|
|
"--lora-backend",
|
|
type=str,
|
|
default="triton",
|
|
help="Choose the kernel backend for multi-LoRA serving.",
|
|
)
|
|
|
|
# Kernel backend
|
|
parser.add_argument(
|
|
"--attention-backend",
|
|
type=str,
|
|
choices=["flashinfer", "triton", "torch_native", "fa3"],
|
|
default=ServerArgs.attention_backend,
|
|
help="Choose the kernels for attention layers.",
|
|
)
|
|
parser.add_argument(
|
|
"--sampling-backend",
|
|
type=str,
|
|
choices=["flashinfer", "pytorch"],
|
|
default=ServerArgs.sampling_backend,
|
|
help="Choose the kernels for sampling layers.",
|
|
)
|
|
parser.add_argument(
|
|
"--grammar-backend",
|
|
type=str,
|
|
choices=["xgrammar", "outlines", "llguidance"],
|
|
default=ServerArgs.grammar_backend,
|
|
help="Choose the backend for grammar-guided decoding.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-flashinfer-mla",
|
|
action="store_true",
|
|
help="Enable FlashInfer MLA optimization",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-flashmla",
|
|
action="store_true",
|
|
help="Enable FlashMLA decode optimization",
|
|
)
|
|
parser.add_argument(
|
|
"--flashinfer-mla-disable-ragged",
|
|
action="store_true",
|
|
help="Not using ragged prefill wrapper when running flashinfer mla",
|
|
)
|
|
|
|
# Speculative decoding
|
|
parser.add_argument(
|
|
"--speculative-algorithm",
|
|
type=str,
|
|
choices=["EAGLE", "EAGLE3", "NEXTN"],
|
|
help="Speculative algorithm.",
|
|
)
|
|
parser.add_argument(
|
|
"--speculative-draft-model-path",
|
|
type=str,
|
|
help="The path of the draft model weights. This can be a local folder or a Hugging Face repo ID.",
|
|
)
|
|
parser.add_argument(
|
|
"--speculative-num-steps",
|
|
type=int,
|
|
help="The number of steps sampled from draft model in Speculative Decoding.",
|
|
default=ServerArgs.speculative_num_steps,
|
|
)
|
|
parser.add_argument(
|
|
"--speculative-eagle-topk",
|
|
type=int,
|
|
help="The number of tokens sampled from the draft model in eagle2 each step.",
|
|
default=ServerArgs.speculative_eagle_topk,
|
|
)
|
|
parser.add_argument(
|
|
"--speculative-num-draft-tokens",
|
|
type=int,
|
|
help="The number of tokens sampled from the draft model in Speculative Decoding.",
|
|
default=ServerArgs.speculative_num_draft_tokens,
|
|
)
|
|
parser.add_argument(
|
|
"--speculative-accept-threshold-single",
|
|
type=float,
|
|
help="Accept a draft token if its probability in the target model is greater than this threshold.",
|
|
default=ServerArgs.speculative_accept_threshold_single,
|
|
)
|
|
parser.add_argument(
|
|
"--speculative-accept-threshold-acc",
|
|
type=float,
|
|
help="The accept probability of a draft token is raised from its target probability p to min(1, p / threshold_acc).",
|
|
default=ServerArgs.speculative_accept_threshold_acc,
|
|
)
|
|
parser.add_argument(
|
|
"--speculative-token-map",
|
|
type=str,
|
|
help="The path of the draft model's small vocab table.",
|
|
default=ServerArgs.speculative_token_map,
|
|
)
|
|
|
|
# Double Sparsity
|
|
parser.add_argument(
|
|
"--enable-double-sparsity",
|
|
action="store_true",
|
|
help="Enable double sparsity attention",
|
|
)
|
|
parser.add_argument(
|
|
"--ds-channel-config-path",
|
|
type=str,
|
|
default=ServerArgs.ds_channel_config_path,
|
|
help="The path of the double sparsity channel config",
|
|
)
|
|
parser.add_argument(
|
|
"--ds-heavy-channel-num",
|
|
type=int,
|
|
default=ServerArgs.ds_heavy_channel_num,
|
|
help="The number of heavy channels in double sparsity attention",
|
|
)
|
|
parser.add_argument(
|
|
"--ds-heavy-token-num",
|
|
type=int,
|
|
default=ServerArgs.ds_heavy_token_num,
|
|
help="The number of heavy tokens in double sparsity attention",
|
|
)
|
|
parser.add_argument(
|
|
"--ds-heavy-channel-type",
|
|
type=str,
|
|
default=ServerArgs.ds_heavy_channel_type,
|
|
help="The type of heavy channels in double sparsity attention",
|
|
)
|
|
parser.add_argument(
|
|
"--ds-sparse-decode-threshold",
|
|
type=int,
|
|
default=ServerArgs.ds_sparse_decode_threshold,
|
|
help="The type of heavy channels in double sparsity attention",
|
|
)
|
|
|
|
# Optimization/debug options
|
|
parser.add_argument(
|
|
"--disable-radix-cache",
|
|
action="store_true",
|
|
help="Disable RadixAttention for prefix caching.",
|
|
)
|
|
parser.add_argument(
|
|
"--disable-cuda-graph",
|
|
action="store_true",
|
|
help="Disable cuda graph.",
|
|
)
|
|
parser.add_argument(
|
|
"--disable-cuda-graph-padding",
|
|
action="store_true",
|
|
help="Disable cuda graph when padding is needed. Still uses cuda graph when padding is not needed.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-nccl-nvls",
|
|
action="store_true",
|
|
help="Enable NCCL NVLS for prefill heavy requests when available.",
|
|
)
|
|
parser.add_argument(
|
|
"--disable-outlines-disk-cache",
|
|
action="store_true",
|
|
help="Disable disk cache of outlines to avoid possible crashes related to file system or high concurrency.",
|
|
)
|
|
parser.add_argument(
|
|
"--disable-custom-all-reduce",
|
|
action="store_true",
|
|
help="Disable the custom all-reduce kernel and fall back to NCCL.",
|
|
)
|
|
parser.add_argument(
|
|
"--disable-mla",
|
|
action="store_true",
|
|
help="Disable Multi-head Latent Attention (MLA) for DeepSeek V2/V3/R1 series models.",
|
|
)
|
|
parser.add_argument(
|
|
"--disable-overlap-schedule",
|
|
action="store_true",
|
|
help="Disable the overlap scheduler, which overlaps the CPU scheduler with GPU model worker.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-mixed-chunk",
|
|
action="store_true",
|
|
help="Enabling mixing prefill and decode in a batch when using chunked prefill.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-dp-attention",
|
|
action="store_true",
|
|
help="Enabling data parallelism for attention and tensor parallelism for FFN. The dp size should be equal to the tp size. Currently only DeepSeek-V2 is supported.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-ep-moe",
|
|
action="store_true",
|
|
help="Enabling expert parallelism for moe. The ep size is equal to the tp size.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-torch-compile",
|
|
action="store_true",
|
|
help="Optimize the model with torch.compile. Experimental feature.",
|
|
)
|
|
parser.add_argument(
|
|
"--torch-compile-max-bs",
|
|
type=int,
|
|
default=ServerArgs.torch_compile_max_bs,
|
|
help="Set the maximum batch size when using torch compile.",
|
|
)
|
|
parser.add_argument(
|
|
"--cuda-graph-max-bs",
|
|
type=int,
|
|
default=ServerArgs.cuda_graph_max_bs,
|
|
help="Set the maximum batch size for cuda graph.",
|
|
)
|
|
parser.add_argument(
|
|
"--cuda-graph-bs",
|
|
type=int,
|
|
nargs="+",
|
|
help="Set the list of batch sizes for cuda graph.",
|
|
)
|
|
parser.add_argument(
|
|
"--torchao-config",
|
|
type=str,
|
|
default=ServerArgs.torchao_config,
|
|
help="Optimize the model with torchao. Experimental feature. Current choices are: int8dq, int8wo, int4wo-<group_size>, fp8wo, fp8dq-per_tensor, fp8dq-per_row",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-nan-detection",
|
|
action="store_true",
|
|
help="Enable the NaN detection for debugging purposes.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-p2p-check",
|
|
action="store_true",
|
|
help="Enable P2P check for GPU access, otherwise the p2p access is allowed by default.",
|
|
)
|
|
parser.add_argument(
|
|
"--triton-attention-reduce-in-fp32",
|
|
action="store_true",
|
|
help="Cast the intermidiate attention results to fp32 to avoid possible crashes related to fp16."
|
|
"This only affects Triton attention kernels.",
|
|
)
|
|
parser.add_argument(
|
|
"--triton-attention-num-kv-splits",
|
|
type=int,
|
|
default=ServerArgs.triton_attention_num_kv_splits,
|
|
help="The number of KV splits in flash decoding Triton kernel. Larger value is better in longer context scenarios. The default value is 8.",
|
|
)
|
|
parser.add_argument(
|
|
"--num-continuous-decode-steps",
|
|
type=int,
|
|
default=ServerArgs.num_continuous_decode_steps,
|
|
help="Run multiple continuous decoding steps to reduce scheduling overhead. "
|
|
"This can potentially increase throughput but may also increase time-to-first-token latency. "
|
|
"The default value is 1, meaning only run one decoding step at a time.",
|
|
)
|
|
parser.add_argument(
|
|
"--delete-ckpt-after-loading",
|
|
action="store_true",
|
|
help="Delete the model checkpoint after loading the model.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-memory-saver",
|
|
action="store_true",
|
|
help="Allow saving memory using release_memory_occupation and resume_memory_occupation",
|
|
)
|
|
parser.add_argument(
|
|
"--allow-auto-truncate",
|
|
action="store_true",
|
|
help="Allow automatically truncating requests that exceed the maximum input length instead of returning an error.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-custom-logit-processor",
|
|
action="store_true",
|
|
help="Enable users to pass custom logit processors to the server (disabled by default for security)",
|
|
)
|
|
parser.add_argument(
|
|
"--tool-call-parser",
|
|
type=str,
|
|
choices=["qwen25", "mistral", "llama3"],
|
|
default=ServerArgs.tool_call_parser,
|
|
help="Specify the parser for handling tool-call interactions. Options include: 'qwen25', 'mistral', and 'llama3'.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-hierarchical-cache",
|
|
action="store_true",
|
|
help="Enable hierarchical cache",
|
|
)
|
|
parser.add_argument(
|
|
"--hicache-ratio",
|
|
type=float,
|
|
required=False,
|
|
default=ServerArgs.hicache_ratio,
|
|
help="The ratio of the size of host KV cache memory pool to the size of device pool.",
|
|
)
|
|
parser.add_argument(
|
|
"--enable-deepep-moe",
|
|
action="store_true",
|
|
help="Enabling DeepEP MoE implementation for EP MoE.",
|
|
)
|
|
|
|
# Server warmups
|
|
parser.add_argument(
|
|
"--warmups",
|
|
type=str,
|
|
required=False,
|
|
help="Specify custom warmup functions (csv) to run before server starts eg. --warmups=warmup_name1,warmup_name2 "
|
|
"will run the functions `warmup_name1` and `warmup_name2` specified in warmup.py before the server starts listening for requests",
|
|
)
|
|
|
|
# Debug tensor dumps
|
|
parser.add_argument(
|
|
"--debug-tensor-dump-output-folder",
|
|
type=str,
|
|
default=ServerArgs.debug_tensor_dump_output_folder,
|
|
help="The output folder for dumping tensors.",
|
|
)
|
|
parser.add_argument(
|
|
"--debug-tensor-dump-input-file",
|
|
type=str,
|
|
default=ServerArgs.debug_tensor_dump_input_file,
|
|
help="The input filename for dumping tensors",
|
|
)
|
|
parser.add_argument(
|
|
"--debug-tensor-dump-inject",
|
|
type=str,
|
|
default=ServerArgs.debug_tensor_dump_inject,
|
|
help="Inject the outputs from jax as the input of every layer.",
|
|
)
|
|
|
|
# Disaggregation
|
|
parser.add_argument(
|
|
"--disaggregation-mode",
|
|
type=str,
|
|
default="null",
|
|
choices=["null", "prefill", "decode"],
|
|
help='Only used for PD disaggregation. "prefill" for prefill-only server, and "decode" for decode-only server. If not specified, it is not PD disaggregated',
|
|
)
|
|
parser.add_argument(
|
|
"--disaggregation-bootstrap-port",
|
|
type=int,
|
|
default=ServerArgs.disaggregation_bootstrap_port,
|
|
help="Bootstrap server port on the prefill server. Default is 8998.",
|
|
)
|
|
|
|
@classmethod
|
|
def from_cli_args(cls, args: argparse.Namespace):
|
|
args.tp_size = args.tensor_parallel_size
|
|
args.dp_size = args.data_parallel_size
|
|
args.ep_size = args.expert_parallel_size
|
|
attrs = [attr.name for attr in dataclasses.fields(cls)]
|
|
return cls(**{attr: getattr(args, attr) for attr in attrs})
|
|
|
|
def url(self):
|
|
if is_valid_ipv6_address(self.host):
|
|
return f"http://[{self.host}]:{self.port}"
|
|
else:
|
|
return f"http://{self.host}:{self.port}"
|
|
|
|
def check_server_args(self):
|
|
assert (
|
|
self.tp_size % self.nnodes == 0
|
|
), "tp_size must be divisible by number of nodes"
|
|
assert not (
|
|
self.dp_size > 1 and self.nnodes != 1 and not self.enable_dp_attention
|
|
), "multi-node data parallel is not supported unless dp attention!"
|
|
assert (
|
|
self.max_loras_per_batch > 0
|
|
# FIXME
|
|
and (self.lora_paths is None or self.disable_cuda_graph)
|
|
and (self.lora_paths is None or self.disable_radix_cache)
|
|
), "compatibility of lora and cuda graph and radix attention is in progress"
|
|
assert self.base_gpu_id >= 0, "base_gpu_id must be non-negative"
|
|
assert self.gpu_id_step >= 1, "gpu_id_step must be positive"
|
|
|
|
if isinstance(self.lora_paths, list):
|
|
lora_paths = self.lora_paths
|
|
self.lora_paths = {}
|
|
for lora_path in lora_paths:
|
|
if "=" in lora_path:
|
|
name, path = lora_path.split("=", 1)
|
|
self.lora_paths[name] = path
|
|
else:
|
|
self.lora_paths[lora_path] = lora_path
|
|
|
|
|
|
def prepare_server_args(argv: List[str]) -> ServerArgs:
|
|
"""
|
|
Prepare the server arguments from the command line arguments.
|
|
|
|
Args:
|
|
args: The command line arguments. Typically, it should be `sys.argv[1:]`
|
|
to ensure compatibility with `parse_args` when no arguments are passed.
|
|
|
|
Returns:
|
|
The server arguments.
|
|
"""
|
|
parser = argparse.ArgumentParser()
|
|
ServerArgs.add_cli_args(parser)
|
|
raw_args = parser.parse_args(argv)
|
|
server_args = ServerArgs.from_cli_args(raw_args)
|
|
return server_args
|
|
|
|
|
|
ZMQ_TCP_PORT_DELTA = 233
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class PortArgs:
|
|
# The ipc filename for tokenizer to receive inputs from detokenizer (zmq)
|
|
tokenizer_ipc_name: str
|
|
# The ipc filename for scheduler (rank 0) to receive inputs from tokenizer (zmq)
|
|
scheduler_input_ipc_name: str
|
|
# The ipc filename for detokenizer to receive inputs from scheduler (zmq)
|
|
detokenizer_ipc_name: str
|
|
|
|
# The port for nccl initialization (torch.dist)
|
|
nccl_port: int
|
|
|
|
# The ipc filename for rpc call between Engine and Scheduler
|
|
rpc_ipc_name: str
|
|
|
|
@staticmethod
|
|
def init_new(server_args, dp_rank: Optional[int] = None) -> "PortArgs":
|
|
port = server_args.port + random.randint(100, 1000)
|
|
while True:
|
|
if is_port_available(port):
|
|
break
|
|
if port < 60000:
|
|
port += 42
|
|
else:
|
|
port -= 43
|
|
|
|
if not server_args.enable_dp_attention:
|
|
# Normal case, use IPC within a single node
|
|
return PortArgs(
|
|
tokenizer_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
|
|
scheduler_input_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
|
|
detokenizer_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
|
|
nccl_port=port,
|
|
rpc_ipc_name=f"ipc://{tempfile.NamedTemporaryFile(delete=False).name}",
|
|
)
|
|
else:
|
|
# DP attention. Use TCP + port to handle both single-node and multi-node.
|
|
if server_args.nnodes == 1 and server_args.dist_init_addr is None:
|
|
dist_init_addr = ("127.0.0.1", server_args.port + ZMQ_TCP_PORT_DELTA)
|
|
elif server_args.dist_init_addr.startswith("["): # ipv6 address
|
|
port_num, host = configure_ipv6(server_args.dist_init_addr)
|
|
dist_init_addr = (host, str(port_num))
|
|
else:
|
|
dist_init_addr = server_args.dist_init_addr.split(":")
|
|
|
|
assert (
|
|
len(dist_init_addr) == 2
|
|
), "please provide --dist-init-addr as host:port of head node"
|
|
|
|
dist_init_host, dist_init_port = dist_init_addr
|
|
port_base = int(dist_init_port) + 1
|
|
if dp_rank is None:
|
|
scheduler_input_port = (
|
|
port_base + 3
|
|
) # TokenizerManager to DataParallelController
|
|
else:
|
|
scheduler_input_port = port_base + 3 + 1 + dp_rank
|
|
|
|
return PortArgs(
|
|
tokenizer_ipc_name=f"tcp://{dist_init_host}:{port_base}",
|
|
scheduler_input_ipc_name=f"tcp://{dist_init_host}:{scheduler_input_port}",
|
|
detokenizer_ipc_name=f"tcp://{dist_init_host}:{port_base + 1}",
|
|
nccl_port=port,
|
|
rpc_ipc_name=f"tcp://{dist_init_host}:{port_base + 2}",
|
|
)
|
|
|
|
|
|
class LoRAPathAction(argparse.Action):
|
|
def __call__(self, parser, namespace, values, option_string=None):
|
|
setattr(namespace, self.dest, {})
|
|
for lora_path in values:
|
|
if "=" in lora_path:
|
|
name, path = lora_path.split("=", 1)
|
|
getattr(namespace, self.dest)[name] = path
|
|
else:
|
|
getattr(namespace, self.dest)[lora_path] = lora_path
|
|
|
|
|
|
class DeprecatedAction(argparse.Action):
|
|
def __init__(self, option_strings, dest, nargs=0, **kwargs):
|
|
super(DeprecatedAction, self).__init__(
|
|
option_strings, dest, nargs=nargs, **kwargs
|
|
)
|
|
|
|
def __call__(self, parser, namespace, values, option_string=None):
|
|
raise ValueError(self.help)
|